Automatic Visualization of Regional Functional Parameters of Left Ventricle from Cardiac Imaging

ABSTRACT

A method for automatically identifying and localizing anatomical regions of a left ventricle of a heart using a segment model, the method including receiving a volumetric MR image series of one or more time-points in a heart cycle with associated myocardial boundaries, representing the heart, performing a long-axis partitioning on each image of the volumetric image series, performing a short-axis partitioning volumetric image series, generating a polar map, wherein each image of the volumetric image series is mapped to a location on the polar map, wherein the location is characterized using the long-axis partitioning and the short-axis partitioning, and generating a mapping from the polar map to a voxel in an associated image of the volumetric image series representing an anatomical location.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/536,616 filed Nov. 8, 2014, which claims the benefit of U.S.Provisional Patent Application No. 62/065,636 filed on Oct. 18, 2014,the complete disclosures of which are herein expressly incorporated byreference in its entirety for all purposes.

BACKGROUND

The present disclosure relates to methods for imaging, and moreparticularly to a method for generating a polar map to visualizeregional functional parameters extracted from cardiac imaging data.

Assessment of left ventricular abnormalities in magnetic resonanceimages is widely accepted as a predictor of cardiac disease, a leadingcause of death worldwide. The quantitative assessment of the leftventricle (LV) includes global and regional measurements.

In most computer-aided diagnosis systems of cardiac magnetic resonanceimaging (MRI), the global functional parameters are commonly available,such as ejection fraction, LV mass, stroke volume, end-diastolic volumeand end-systolic volume.

The LV regional assessment typically relies on the visual inspection,analysis and interpretation of cine images of the left ventricle inmultiple planes and the interpretation of LV regional functions. Studieshave shown that this method may be inaccurate, time consuming and sufferfrom high inter-observer variability.

BRIEF SUMMARY

According to an exemplary embodiment of the present invention, a methodfor automatically identifying and localizing anatomical regions of aleft ventricle of a heart using a segment model, the method includingreceiving a volumetric image series of one or more time-points in aheart cycle with associated myocardial boundaries, representing theheart, performing a long-axis partitioning on each image of thevolumetric image series, performing a short-axis partitioning on eachimage of the volumetric image series, generating a polar map, whereineach image of the volumetric image series is mapped to a location on thepolar map, wherein the location is characterized using the long-axispartitioning and the short-axis partitioning, and generating a mappingfrom the polar map to a voxel in an associated image of the volumetricimage series representing an anatomical location.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Preferred embodiments of the present invention will be described belowin more detail, with reference to the accompanying drawings:

FIG. 1 is a polar map according to an exemplary embodiment of thepresent invention;

FIG. 2 shows a basal segmentation according to an exemplary embodimentof the present invention;

FIG. 3 shows a mid-cavity segmentation according to an exemplaryembodiment of the present invention;

FIG. 4 shows an apical segmentation according to an exemplary embodimentof the present invention;

FIG. 5 shows region layers in a long axis plane according to anexemplary embodiment of the present invention;

FIG. 6 is a flowchart of a method according to an exemplary embodimentof the present invention;

FIGS. 7-10 illustrate of a method of generating a boundary of eachsegment according to an exemplary embodiment of the present invention;

FIG. 11 is a polar map according to an exemplary embodiment of thepresent invention; and

FIG. 12 is a diagram of a computer system configured for feedbackcollection and analysis according to an exemplary embodiment of thepresent invention.

DETAILED DESCRIPTION

According to an exemplary embodiment of the present invention,functional parameters of a heart are visualized in cardiac MRI using a2-dimensional (2D) polar map (see FIG. 1). Cardiac magnetic resonance(MR) images are 3D+cine images, which enable the assessment of a beatingheart. According to an exemplary embodiment of the present invention, a1-to-1 linkage is built from volumetric cardiac MR images series of oneor more time-points in the heart cycle to a 2D color-coded polar map.Various functional parameters can be represented in the polar map, suchas the LV wall motion and thickness.

The LV regional assessment typically relies on the visual inspection,analysis and interpretation of cine images of the left ventricle inmultiple planes and the interpretation of LV regional functions. Forexample, LV wall (myocardium) motion is an important regional assessmentin evaluating the LV. The diagnosis of abnormal LV wall motion isgenerally based on the reading of cardiac magnetic resonance (MR) cineimages. In clinical practice, heart wall motion is scored by following astandard issued by American Heart Association (AHA), where 17 myocardialsegments are graded from cine-MR images in the short-axis plane—cardiacslices from the base (top) (FIG. 2) to the apex (bottom) (FIG. 4). Amapping of a 17 segment model to a polar map is shown in FIG. 2 throughFIG. 4. FIG. 5 shows region layers of FIG. 2 through FIG. 4 in thelong-axis plane.

According to an exemplary embodiment of the present invention, anautomated segment extraction and visualization system generates a polarmap to visually evaluate measurements within specific anatomicalregions. Extracted segments can be used in further automated analysisand diagnosis, such as hypertrophy (LV wall thickening) detection orinfarct transmurality (level of LV tissue damage due to a heart attack).Extracted segments can be automatically correlated across differentimaging modalities as well as different image series over time.

According to an exemplary embodiment of the present invention, theautomated segment extraction and visualization system includes aframework that automatically identifies and localizes a plurality ofanatomical regions of the left ventricle. Any functional measurements,such as motion, thickness, etc., performed on these regions can be usedto automatically generate a polar map in which the measurements can berepresented by color range, shading, different patterns, etc. In thepolar map, anatomical location is directly related to the specificsegment in the model. The polar map is used to visualize the regionalfunctional parameters. The polar map has a 1-to-1 linkage to thecorresponding location within the heart. The polar map automaticallydirects a user to tissue or its functional parameters from acorresponding location on the polar map.

In one or more embodiments of the present invention, a method forautomatically generating a polar map of the LV in a segment model (seeFIG. 6) includes long-axis partitioning 602, short-axis partitioning603, and polar map generation 604. The method is applied on all 2D MRslices of a certain time phase in a cine cardiac MRI series, such asend-diastole. The method takes input 601 including a volumetric MR imageseries 605 with the associated myocardial boundaries 606. The methodoutputs a segment mask 607 for all tissue in the LV, a polar map 611 torepresent the regional functional parameters extracted from the LV 610,and a mapping from the polar map to the anatomical location in thecardiac MRI series 612. FIG. 6 is the flowchart of an exemplaryframework.

At block 601, the input includes a volumetric MR image series 605. Thevolumetric MR image series includes 2D MR slices in the short-axisplane. The slices are from the same cardiac phase. The input 601 furtherincludes the corresponding inner and outer LV wall boundaries 606.

At block 602, long-axis partitioning includes labeling each sliceaccording to its location from the base (top) to the apex (bottom) ofthe heart. These locations can include basal, mid-cavity, apical andapex.

At block 613, mid-cavity layer identification is performed. Themid-cavity is selected from the region that includes the entire lengthof the papillary muscles (PMs) at end-diastole (ED). The PMs are used asanatomic landmarks to select mid-cavity slices. This involvesidentifying candidate regions of PMs by subtracting endocardium (innerLV wall) from blood pool masks, using, for example a Ostu thresholdingor other image thresholding method. The candidate regions are examinedfor size. If the regions are determined to be larger than a threshold,e.g., via morphological processing, than they are considered as PMs. Anyslices that were found to have PMs are selected as belonging to themid-cavity.

At block 614, apex layer identification is performed. A feature of theapex is an area of the myocardium beyond the end of the left ventricularcavity where this is no blood pool. On a typical MR slice, blood poolsappear as a relatively bright area and myocardium and surroundingstructures appear as a relatively dark area. Based on these features,the apex can be localized by detecting if a blood pool exists. The bloodpool is the area inside the inner LV wall boundary, which is availablefrom the input 601. If the slice does not have inner wall the blood pooldoes not exist.

At block 615, basal and apical layer identification is performed. Basaland apical slices are determined by detecting the orientation of thelong axis of the LV based on the endocardium size in image slices eitherside of the mid-cavity layer. The image slices on both sides of themid-cavity are grouped separately and the average endocardium size iscalculated for each group. The larger average size group is identifiedas the basal layer, whereas the smaller average size group is identifiedas the apical layer.

At block 603, short-axis partitioning is performed. Following block 602,slices are labeled with a layer and the slices within the layer aresequential. Each slice is partitioned into regions to identify acircumferential location. Short-axis partitioning includes septumextraction 616, correction of segments for apical slices 617 andlabeling of segments 618.

In the AHA 17 segment model the basal and mid-cavity slices are dividedinto 6 segments of approximately 60 degrees taking into considerationall of the septum landmarks, whereas the apical slices have 4 segmentsand the 17th segment is the apex. The AHA 17 segment model specifiesthat the upper and lower junction between the right ventricular wall andthe inter-ventricular septum of the basal (and mid-cavity) slices at theend-diastolic phase can be used to identify different segments. For eachimage, there are four sub-steps to locate the anterior and inferior endsof the inter-ventricular septum. FIG. 7 through FIG. 10 illustrate theprocess.

FIG. 7 shows the generation of a cylinder 701-702 for each slice inbasal, mid-cavity and apical layers. The center of the inner circle 701is the centroid of the endocardium (LV inner wall). The inner circleradius r₁ is the mean radial length of the LV inner boundary from itscentroid. The outer circle 702 radius r₂=r₁+e millimeters, where e is aparameter to the system that ensures that the region of interest (ROI)completely encapsulates the myocardium even if it is abnormally thick. Aclinically normal LV thickness is 12 mm or less, and therefore e shouldbe greater than 12. The cylinder ROI includes three types of pixelsrepresenting the right ventricle (RV) blood pool, the myocardium and thelung.

FIG. 8 shows a polar transformation applied to the ROI to form arectangular image 801. Transforming the image into a polar coordinatesystem eases calculation and visualization. In one or more embodiments,the RV area is split into two parts after transformation. Therefore, therectangular image is duplicated and joined by the same image to avoidthis issue. The RV blood pool and myocardium occupy the major part ofthe ROI region; hence, the intensity distribution can be classified. TheROI is binarized using Otsu thresholding (802). The largest region isthen selected as the RV blood pool (803). Its edges are identified tolocate the two ends of the septum.

FIG. 9 shows the septal region mapped back to the original image. Twolines are generated, A and C, representing the anterior and inferiorends of the inter-ventricular septum respectively as determined by theedges identified in 803.

At block 617, the septal extraction operates under the assumption thatthe RV blood pool is the largest bright region in the cylinder ROI. Insome of the slices of the apical layer, the blood pool becomes small ordoes not exist at all as the slice is below the right ventricle andthere is no actual blood pool. This can lead to variation among theresulting segments of apical slices with the initial septal extraction.To deal with this problem, additional measures are used. The slices inthe mid-cavity layer have a relatively bigger right ventricle blood poolcompared to other slices. The septum extracted from those slices isconsidered to be more reliable than from other slices. A check isperformed as to whether the septum extracted in the basal and apicallayers is consistent with the mid-cavity layer by measuring the shift indetected septal regions between adjacent slices. If the shift is above athreshold, the septal boundary found in the previous slice is used as areference point to the one in the current slice.

Starting with the first slice in the apical layer adjacent to themid-cavity, the distance of each of the extracted lines (A_(curr) andC_(curr)) in the current slice from their respective location in theprevious slice (A_(pre) and C_(pre)) is determined. The assumption isthat the distance should be small enough such that the septums extractedin the two slices are consistent. If the distance is bigger than theslice thickness, it suggests that the septum is not extracted properlyin that slice. The lines A_(curr) and C_(curr) need to be recalculated.Let A_(inner,curr); A_(outer,curr) be the start and end point of theline A, respectively. In order to find the updated line A,A_(inner,curr) is set to be point on the LV inner boundary that isclosest to A_(inner,pre) in the previous slice. A_(outer,curr) is thepoint on the LV outer boundary that is closest to A_(outer,pre). Thesame process is applied to update C_(curr) using C_(pre) on the previousslice and the entire routine is repeated for each slice in the apicallayer.

At block 618, segment labeling is performed. Given the upper and lowerLV/RV junction points determined in the aforementioned steps, segments 2and 3 (FIGS. 2) and 8 and 9 (FIG. 3) are found by dividing evenly theregion between the two junction points along the circumference of theROI (line B in FIG. 10), and the other four segments (1, 4, 5, 6 inFIGS. 2 and 7, 10, 11, 12 in FIG. 3) are extracted by finding theopposite lines (180° along the circumference of the ROI) correspondingto the first three segment lines in the septal region (lines D, E and Fin FIG. 10). For segments 13, 15 and 16 (FIG. 4), only the oppositelines of A and C (FIG. 9) are determined. To detect the orientation ofthe LV and allow segment labeling, the lung is segmented in one of thebasal or mid-cavity slices. Lung tissue has a relatively low signal inMR images and appears as dark areas. In the chosen slice, a new circularROI is extracted with the center as the centroid of the endocardiumboundary and with a radius off mm, where f is a parameter to the systemthat is large enough to include a significant portion of lung tissue inthe ROI. It should be understood that f can be selected by one ofordinary skill in the art and that the “significant portion of the lung”would be understood, for example, to balance the inclusion of portionsof the lung tissue in the ROI while excluding other tissue(s). The ROIis thresholded to obtain a binary mask where the biggest component isextracted and identified as the lung. The distance between the centroidof the two lines (A and C) and the centroid of the lung are computed.The line with the shorter distance to the lung is labeled as thedivision between the anteroseptal and anterior region while the other asthe line that separates the inferoseptal and inferior regions. Thus, thecircumferential locations in the basal and mid-cavity are determined.The circumferential locations are: anterior, anteroseptal, inferoseptal,inferior, inferolateral, and anterolateral; for apical, the foursegments are: anterior, septal, inferior, and lateral. Table 1 shows theassignment strategy:

TABLE 1 The assignment of regions by using information from layer ID andextracted lines. The assumption is: A is the line that represents theend of the inter-ventricular septum closest to the lung. ID layersub-region Lines 1 basal anterior F−>A 2 basal anteroseptal A−>B 3 basalinferoseptal B−>C 4 basal inferior C−>D 5 basal inferolateral D−>E 6basal anterolateral E−>F 7 mid-cavity anterior F−>A 8 mid-cavityanteroseptal A−>B 9 mid-cavity inferoseptal B−>C 10 mid-cavity inferiorC−>D 11 mid-cavity inferolateral D−>E 12 mid-cavity anterolateral E−>F13 apical anterior F−>A 14 apical septal A−>C 15 apical inferior C−>D 16apical lateral D−>F 17 apex — —

At block 607, the output 1 includes a 3D segment mask. All tissue in LVis assigned to an anatomical segment label.

At block 608, a measurement function is applied to the 3D segment mask607 to determine the value of the regional functional parameter todisplay in the polar map 611. Optional additional inputs 609 to thisfunction may include other 3D segment masks generated by the method inFIG. 6 (blocks 601 to 603) but using as input a volumetric MR imageseries 605, and associated wall boundaries 606 from a different timepoint in the heart cycle. Other optional inputs 609 could be any relateddata required to calculate the specific measurement function. Operatingupon multiple inputs in the measurement function allows the display inthe polar map 611 of relative regional functional parameters such as LVwall (myocardium) motion.

At block 610, a regional linear mapping (image→polar map) is performed.In the polar map, the anatomical locations are superimposed and areconsistent with the AHA 17 segment standard. In the previous steps, thesegments have been automatically extracted from all slices at a certaintime phase. In order to plot the feature extracted from a slice to thepolar map, the radius and angle for each voxel is determined. A standardpolar map 100, with 17 segments (shown in FIG. 1) is generated. Theradius R of each layer l is determined: Let apex layer R₁=0.5 unit,apical layer R₂=1.5 unit, mid-cavity layer R₃=2.5 unit and basal layerR₄=3.5 unit. Let n_(l) be the total number of slices in layer l, theradius r_(s,l) at s-th slice in layer l is defined as,

$r_{s,l} = {\frac{s\left( {R_{l} - R_{l - 1}} \right)}{n_{l}} + R_{l - 1}}$

where R₀=0, for all l in the range of [1 2 3 4].

After the radius of a slice has been determined, the informationdetermined for each anatomical region is linearly mapped to thecorresponding regions in the segment polar map. The same strategy isthen applied to all slices. A segment polar map with parameter map isthen generated as shown in FIG. 11, where here the color gradient(illustrated in grey-scale) relates to the thickness of the myocardium(heart wall) as an example. In FIG. 11 darker areas (e.g., 1102) reflectthicker myocardium and mid-range and lighter areas (e.g., 1101) reflectnormal myocardium.

At block 612, the regional linear mapping (e.g., from the polar map tothe image) is performed. This process associates all pixels in the polarmap to the corresponding tissue in the MR images that are used togenerate the polar map. The 2-way link of the polar map and the MRimages allows the user to click on any pixel in the polar map to findthe corresponding location in a specific MR image, and vice versa.

Let P be a point in the polar map, the goal is to compute the location kon the s-th slice in the MR image series. In the flowchart in FIG. 6, inblock 602 it is known to which layer each image slice belongs. From thepolar map, let l be the layer ID and r the radius of the chosen point.Based on the above information the slice ID, s can be determined as,

s=floor((r−R _(l−1))*n _(l)/(R _(l) −R _(l−1)))

where R_(l) is the radius of the layer l, and n_(l) is the number ofslices in that layer that is obtained from block 602 in FIG. 6.

After the slice ID of the point P is determined, the location k on thatslice needs to be generated. In the process shown in FIGS. 7-10, theboundaries of each segment (Lines A, B, C, D, E, F) are obtained. Bylinearly mapping the point P in its selected segment to thecorresponding segment in the LV, the location on the s-th slice can befound.

At block 611, the output includes a 2-D polar map to present regionalfunctional parameters of the LV. The 2-D polar map offers featuresincluding:

1. The functional measurements or parameters for all LV tissue arepresented in the polar map with the label of the anatomical regions. Thepolar map allows a user to visualize the overall distribution of thefunctional parameters, especially identify suspicious regions withabnormal parameters.

2. The polar map is able to visualize the granular details for alltissue in the LV.

3. The polar map has a 2-way 1-to-1 mapping to the correspondinglocation in the MR image. It allows the users to find the regions in theMR image that shows abnormal parameters in the polar map. In this way,the users not only are able to see the overall distribution of theparameters with the segment labeled, but they can also directly go tothe corresponding location in the MR image (e.g., by clicking on theproblematic regions on the polar map).

It should be understood that the methodologies of embodiments of theinvention may be particularly well-suited for automatic segmentextraction and visualization. Furthermore, the disclosure is not limitedto MRI and embodiments are application to other imaging systems, such ascomputed tomography (CT), positron emission tomography-computedtomography (PET-CT), and the like.

By way of recapitulation, according to an exemplary embodiment of thepresent invention, a method for automatically generating a polar map ofthe LV in a segment model (see FIG. 6) includes long-axis partitioning602, short-axis partitioning 603, and polar map generation 604. Themethod is applied on all 2D MR slices of a certain time phase in a cinecardiac MRI, such as end-diastole. The method takes input 601 includinga volumetric MR image series 605 with the associated myocardialboundaries 606. The method outputs a parametric segment mask 607 for alltissue in the LV, a polar map 611 to represent the regional functionalparameters extracted from the LV 610, and a mapping from the polar mapto the anatomical location in the cardiac MRI series 612.

The methodologies of embodiments of the disclosure may be particularlywell-suited for use in an electronic device or alternative system.Accordingly, embodiments of the present invention may take the form ofan entirely hardware embodiment or an embodiment combining software andhardware aspects that may all generally be referred to herein as a“processor,” “circuit,” “module” or “system.”

Furthermore, it should be noted that any of the methods described hereincan include an additional step of providing a system for feedbackcollection and analysis. Further, a computer program product can includea tangible computer-readable recordable storage medium with code adaptedto be executed to carry out one or more method steps described herein,including the provision of the system with the distinct softwaremodules.

Referring to FIG. 12; FIG. 12 is a block diagram depicting an exemplarycomputer system 1200 for automatic segment extraction according to anembodiment of the present invention. The computer system shown in FIG.12 includes a processor 1201, memory 1202, display 1203, input device1204 (e.g., keyboard), a network interface (I/F) 1205, a media IF 1206,and media 1207, such as a signal source, e.g., camera, Hard Drive (HD),external memory device, etc.

In different applications, some of the components shown in FIG. 12 canbe omitted. The whole system shown in FIG. 12 is controlled by computerreadable instructions, which are generally stored in the media 1207. Thesoftware can be downloaded from a network (not shown in the figures),stored in the media 1207. Alternatively, software downloaded from anetwork can be loaded into the memory 1202 and executed by the processor1201 so as to complete the function determined by the software.

The processor 1201 may be configured to perform one or moremethodologies described in the present disclosure, illustrativeembodiments of which are shown in the above figures and describedherein. Embodiments of the present invention can be implemented as aroutine that is stored in memory 1202 and executed by the processor 1201to process the signal from the media 1207. As such, the computer systemis a general-purpose computer system that becomes a specific purposecomputer system when executing routines of the present disclosure.

Although the computer system described in FIG. 12 can support methodsaccording to the present disclosure, this system is only one example ofa computer system. Those skilled of the art should understand that othercomputer system designs can be used to implement embodiments of thepresent invention.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Although illustrative embodiments of the present invention have beendescribed herein with reference to the accompanying drawings, it is tobe understood that the invention is not limited to those preciseembodiments, and that various other changes and modifications may bemade therein by one skilled in the art without departing from the scopeof the appended claims.

What is claimed is:
 1. A method for automatically identifying andlocalizing anatomical regions of a left ventricle of a heart using asegment model, the method comprising: receiving a volumetric imageseries of one or more time-points in a heart cycle with associatedmyocardial boundaries, representing the heart; performing a long-axispartitioning on each image of the volumetric image series; performing ashort-axis partitioning on each image of the volumetric image series;generating a polar map, wherein each image of the volumetric imageseries is mapped to a location on the polar map, wherein the location ischaracterized using the long-axis partitioning and the short-axispartitioning; and generating a mapping from the polar map to a voxel inan associated image of the volumetric image series representing ananatomical location, wherein the mapping is displayed.
 2. The method ofclaim 1, further comprising automatically generating the polar map. 3.The method of claim 1, wherein a functional measurement is representedin the polar map by a color range and the anatomical location isdirectly related to a specific segment of the polar map.
 4. The methodof claim 1, wherein the polar map displays regional functionalparameters.
 5. The method of claim 1, the polar map has a 1-to-1 linkageto a corresponding image of the volumetric image series.
 6. The methodof claim 5, further comprising receiving an indication of a location onthe polar map and outputting a corresponding tissue or its functionalparameters.
 7. A method for automatically identifying and localizinganatomical regions of a left ventricle of a heart using a segment model,the method comprising: receiving a volumetric image series of one ormore time-points in a heart cycle with associated myocardial boundaries,representing the heart; performing a long-axis partitioning on eachimage of the volumetric image series; performing a short-axispartitioning on each image of the volumetric image series; generating apolar map, wherein the polar map is the segment model having a pluralityof segments defining locations of portions of the heart, wherein eachimage of the volumetric image series is mapped to a correspondingsegment on the polar map, wherein each of the plurality of segments ischaracterized using the long-axis partitioning and the short-axispartitioning; and generating a mapping from the segments of the polarmap to a voxel in an associated image of the volumetric image seriesrepresenting an anatomical location, wherein the mapping is displayed.8. The method of claim 7, further comprising automatically generatingthe polar map of the heart.
 9. The method of claim 7, wherein afunctional measurement is represented in the polar map by a color rangeand the anatomical location is directly related to a specific segment ofthe polar map.
 10. The method of claim 7, wherein the polar map displaysregional functional parameters.
 11. The method of claim 7, the polar maphas a 1-to-1 linkage to a corresponding image of the volumetric imageseries.
 12. The method of claim 11, further comprising receiving anindication of a location on the polar map and outputting a correspondingtissue or its functional parameters.